Baselines.  
We compare our method to the following baselines. We evaluate the effect on the model output when it is customized by shifting the latent \(w_\text{new} = w + \Delta w\)  using a constant vector \(\Delta w\)  derived by averaging samples that resemble the user sketch, similar to the vector arithmetic method proposed in Radford et al.  [1]}:
\(\Delta w = \mathbb {E}_\text{match}[w] - \mathbb {E}_\text{orig}[w]\) .
Here \( \mathbb {E}_\text{orig}[w]\)  is the mean latent in the original unmodified model and \( \mathbb {E}_\text{match}[w]\)  is the mean latent over a subset of samples where the image resembles the user sketch according to a simple criterion. We implement two baseline variants, which use different methods to sample \( \mathbb {E}_\text{match}[w]\) . (1) Baseline (SBIR): selects best matched samples using the sketch-based image retrieval method by Bui et al.  [2]}, (2) Baseline (Chamfer): matches samples using the symmetric Chamfer distance \(d(x, y) + d(y, x)\)  between the input sketch \(y\)  and a sketch of the image \(x\)  as computed by PhotoSketch [3]}. To estimate \( \mathbb {E}_\text{match}[w]\) , we take the top-matched 10,000 images from 1 million samples, and we score each image using the minimum distance to all user sketches.
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